Article published in:
Bridging the Methodological Divide: Linguistic and psycholinguistic approaches to formulaic language
Edited by Stefanie Wulff and Debra Titone
[The Mental Lexicon 9:3] 2014
► pp. 437–472

N-gram probability effects in a cloze task

| University of Tübingen
| University of Tübingen / University of Alberta
| University of Alberta
What knowledge influences our choice of words when we write or speak? Predicting which word a person will produce next is not easy, even when the linguistic context is known. One task that has been used to assess context dependent word choice is the fill-in-the-blank task, also called the cloze task. The cloze probability of specific context is an empirical measure found by asking many people to fill in the blank. In this paper we harness the power of large corpora to look at the influence of corpus-derived probabilistic information from a word’s micro-context on word choice. We asked young adults to complete short phrases called n-grams with up to 20 responses per phrase. The probability of the responded word and the conditional probability of the response given the context were predictive of the frequency with which each response was produced. Furthermore the order in which the participants generated multiple completions of the same context was predicted by the conditional probability as well. These results suggest that word choice in cloze tasks taps into implicit knowledge of a person’s past experience with that word in various contexts. Furthermore, the importance of n-gram conditional probabilities in our analysis is further evidence of implicit knowledge about multi-word sequences and support theories of language processing that involve anticipating or predicting based on context.
Keywords: formulaic language, multi-word expressions, n-grams, cloze probability, production
Published online: 23 January 2015
https://doi.org/10.1075/ml.9.3.04sha
References

References

Akaike, H.
(1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. CrossrefGoogle Scholar
Arnon, I., & Cohen Priva, U.
(2013) More than words: The effect of multi-word frequency and constituency on phonetic duration. Language and Speech, 56(3), 349–371. CrossrefGoogle Scholar
Arnon, I., & Snider, N.
(2010) More than words: frequency effects for multi-word phrases. Journal of Memory and Language, 62(1), 67–82. CrossrefGoogle Scholar
Baayen, R.H.
(2008) Analyzing linguistic data: A practical introduction to statistics using R. Cambridge, UK: Cambridge University Press. CrossrefGoogle Scholar
(2010) Demythologizing the word frequency effect: A discriminative learning perspective. The Mental Lexicon, 5(3), 436–461. CrossrefGoogle Scholar
Baayen, R.H., Hendrix, P., & Ramscar, M.
(2013) Sidestepping the combinatorial explosion: An explanation of n-gram frequency effects based on naive discriminative learning. Language and Speech, 56(3), 329–347. CrossrefGoogle Scholar
Baayen, R.H., Milin, P., Djurdjevic, D., Hendrix, P., & Marelli, M.
(2011) An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118(3), 438–481. CrossrefGoogle Scholar
Bar, M.
(2007) The proactive brain: Using analogies and associations to generate predictions. Trends in Cognitive Sciences, 11(7), 280–289. CrossrefGoogle Scholar
Bates, D., Mächler, M., & Bolker, B.
(2011) lme4: linear mixed-effects models using S4 classes. Retrieved from http://​cran​.r​-project​.org​/web​/packages​/lme4/.
Battig, W., & Montague, W.
(1969) Category norms of verbal items in 56 categories: A replication and extension of the Connecticut category norms. Journal of Experimental Psychology, 80, 1–46. CrossrefGoogle Scholar
Beattie, G., & Butterworth, B.
(1979) Contextual probability and word frequency as determinants of pauses and errors in spontaneous speech. Language and Speech, 22(3), 201.Google Scholar
Belsley, D.A., Kuh, E., & Welsch, R.E.
(2004) Regression diagnostics: Identifying influential data and sources of collinearity. Hoboken, NJ, USA: Wiley-Interscience.Google Scholar
Block, C., & Baldwin, C.
(2010) Cloze probability and completion norms for 498 sentences: Behavioral and neural validation using event-related potentials. Behavior research methods, 42(3), 665–670. CrossrefGoogle Scholar
Bloom, P., & Fischler, I.
(1980) Completion norms for 329 sentence contexts. Memory and Cognition, 8(6), 631–642. CrossrefGoogle Scholar
Bormuth, J.
(1966) Readability: A new approach. Reading Research Quarterly, 1, 79–132. CrossrefGoogle Scholar
Brants, T., & Franz, A.
(2006) Web 1T 5-gram version 1. Philadelphia, PA USA: Linguistic Data Consortium.Google Scholar
Chambers, J.M.
(1992) Linear models. In J.M. Chambers & T.J. Hastie (Eds.), Statistical models in S (Chap. 4). USA, NY: Wadsworth & Brooks.Google Scholar
Chou, Y.M., Polansky, A.M., & Mason, R.L.
(1998) Transforming non-normal data to normality in statistical process control. Journal of Quality Technology, 30(2), 133–141.Google Scholar
Conway, C.M., Bauernschmidt, A., Huang, S., & Pisoni, D.
(2010) Implicit statistical learning in language processing: word predictability is the key. Cognition, 114(3), 356–371. CrossrefGoogle Scholar
Criss, A., Aue, W., & Smith, L.
(2010) The effects of word frequency and context variability in cued recall. Journal of Memory and Language, 64(2), 119–132. CrossrefGoogle Scholar
Crowe, S.
(1998) Decrease in performance on the verbal fluency test as a function of time: Evaluation in a young healthy sample. Journal of Clinical and Experimental Neuropsychology, 20(3), 391–401. CrossrefGoogle Scholar
DeLong, K., Urbach, T., & Kutas, M.
(2005) Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8), 1117. CrossrefGoogle Scholar
Dilkina, K., McClelland, J.L., & Plaut, D.C.
(2010) Are there mental lexicons? The role of semantics in lexical decision. Brain Research, 1365, 66–81. CrossrefGoogle Scholar
Ellis, W.
(1999) A source book of Gestalt psychology. London, UK: Psychology Press.Google Scholar
Elman, J.
(2011) Lexical knowledge without a lexicon? The Mental Lexicon, 6(1), 1–33. CrossrefGoogle Scholar
Fano, R.M., & Hawkins, D.
(1961) Transmission of information: A statistical theory of communications. American Journal of Physics, 29, 793. CrossrefGoogle Scholar
Fillenbaum, S., Jones, L., & Rapoport, A.
(1963) The predictability of words and their grammatical classes as a function of rate of deletion from a speech transcript1. Journal of Verbal Learning and Verbal Behavior, 2(2), 186–194. CrossrefGoogle Scholar
Finn, P.
(1977) Word frequency, information theory, and cloze performance: A transfer feature theory of processing in reading. Reading Research Quarterly, 13(4), 508–537. CrossrefGoogle Scholar
Francis, W., & Kucera, H.
(1982) Frequency analysis of English usage. Boston, MA, USA: Houghton Mifflin Company.Google Scholar
Frank, S.L., & Bod, R.
(2011) Insensitivity of the human sentence-processing system to hierarchical structure. Psychological Science, 22(6), 829–834. CrossrefGoogle Scholar
Griffin, Z., & Bock, K.
(1998) Constraint, word frequency, and the relationship between lexical processing levels in spoken word production. Journal of Memory and Language, 38(3), 313–338. CrossrefGoogle Scholar
Hahn, L.W., & Sivley, R.M.
(2011) Entropy, semantic relatedness and proximity. Behavior Research Methods, 43(3), 746–760. CrossrefGoogle Scholar
Hay, J., Pelucchi, B., Estes, K., & Saffran, J.
(2011) Linking sounds to meanings: Infant statistical learning in a natural language. Cognitive Psychology, 63(2), 93–106. CrossrefGoogle Scholar
Kamide, Y.
(2008) Anticipatory processes in sentence processing. Language and Linguistics Compass, 2(4), 647. CrossrefGoogle Scholar
Kučera, H., & Francis, W.
(1967) Computational analysis of present-day American English. Dartmouth, NH, USA: Dartmouth Publishing Group.Google Scholar
Kutas, M., & Hillyard, S.
(1984) Brain potentials during reading reflect word expectancy and semantic association. Nature, 307(5947), 161–163. CrossrefGoogle Scholar
McEvoy, C.L., Nelson, D.L., & Komatsu, T.
(1999) What is the connection between true and false memories? The differential roles of inter item associations in recall and recognition. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(5), 1177. CrossrefGoogle Scholar
McKenna, M.C.
(1986) Cloze procedure as a memory-search process. Journal of Educational Psychology, 78, 433–440. CrossrefGoogle Scholar
Mirman, D., Graf Estes, K., & Magnuson, J.
(2010) Computational modeling of statistical learning: Effects of transitional probability versus frequency and links to word learning. Infancy, 15(5), 471–486. CrossrefGoogle Scholar
Nelson, D.L., McEvoy, C.L., & Dennis, S.
(2000) What is free association and what does it measure? Memory & Cognition, 28(6), 887–899. CrossrefGoogle Scholar
Nelson, D.L., McEvoy, C.L., & Schreiber, T.A.
(1998) The University of South Florida word association, rhyme, and word fragment norms. http://​www​.usf​.edu​/FreeAssociation/.
Nelson, D.L., McKinney, V., Gee, N., & Janczura, G.
(1998) Interpreting the influence of implicitly activated memories on recall and recognition. Psychological Review, 105(2), 299. CrossrefGoogle Scholar
Norris, D., & Kinoshita, S.
(2008) Perception as evidence accumulation and Bayesian inference: Insights from masked priming. Journal of Experimental Psychology: General, 137(3), 434–455. CrossrefGoogle Scholar
Owens, M., O’Boyle, P., McMahon, J., Ming, J., & Smith, F.
(1997) A comparison of human and statistical language model performance using missing-word tests. Language and Speech, 40(4), 377.Google Scholar
Pickering, M., & Garrod, S.
(2007) Do people use language production to make predictions during comprehension? Trends in Cognitive Sciences, 11(3), 105–110. CrossrefGoogle Scholar
R. Development Core Team
(2009) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Ramscar, M., & Gitcho, N.
(2007) Developmental change and the nature of learning in childhood. Trends in Cognitive Science, 11(7), 274–279. CrossrefGoogle Scholar
Ruff, R., Light, R., Parker, S., & Levin, H.
(1997) The psychological construct of word fluency. Brain and Language, 57(3), 394–405. CrossrefGoogle Scholar
Saffran, J.R., Aslin, R.N., & Newport, E.L.
(1996) Statistical learning by 8-month-old infants. Science, 274, 1926–1928. CrossrefGoogle Scholar
Schwanenflugel, P., & LaCount, K.
(1988) Semantic relatedness and the scope of facilitation for upcoming words in sentences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(2), 344. CrossrefGoogle Scholar
Shannon, C.E.
(1948) A mathematical theory of communication. Bell System Technical Journal, 27, 379–423. CrossrefGoogle Scholar
(1951) Prediction and entropy of printed English. Bell System Technical Journal, 30(1), 50–64. CrossrefGoogle Scholar
Shaoul, C., & Westbury, C.F.
(2011) Formulaic sequences: Do they exist and do they matter? The Mental Lexicon, 6(1), 171–196. CrossrefGoogle Scholar
Shaoul, C., Westbury, C.F., & Baayen, R.H.
(2013) The subjective frequency of word n-grams. Psihologija, 46(4), 497–537. CrossrefGoogle Scholar
Smith, N.J.
(2011) Scaling up psycholinguistics. Unpublished Doctoral Dissertation Downloaded in December, 2013 from http://​vorpus​.org/. San Diego, CA, USA: University of California, San Diego.Google Scholar
Smith, N.J., & Levy, R.
(2011) Cloze but no cigar: The complex relationship between cloze, corpus, and subjective probabilities in language processing. In Proceedings of the 33rd annual meeting of the cognitive science conference (pp. 1637–1642).
Sprenger, S., & van Rijn, H.
(2013) It’s time to do the math: Computation and retrieval in phrase production. The Mental Lexicon, 8(1), 1–25. CrossrefGoogle Scholar
Taylor, W.
(1953) “Cloze procedure”: A new tool for measuring readability. Journalism Quarterly, 30(4), 415–433.Google Scholar
Tremblay, A., & Tucker, B.V.
(2011) The effects of N-gram probabilistic measures on the recognition and production of four-word sequences. The Mental Lexicon, 6(2), 302–324. CrossrefGoogle Scholar
Willems, R., & Hagoort, P.
(2007) Neural evidence for the interplay between language, gesture, and action: A review. Brain and Language, 101(3), 278–289. CrossrefGoogle Scholar
Wood, S.
(2006) Generalized additive models: An introduction with R. USA, NY: CRC Press.Google Scholar
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